Implementasi Metode Improved K-Means dengan Algoritma Dbscan untuk Pengelompokan Film

Abstract

The Indonesian film industry continues to experience an increase seen from the number of films that appear in theaters today with a box office increase of 28 percent each year in the past four years. Internet Movie Database (IMDb) is a website that provides information about films around the world, including the people involved in it from actors, directors, writers to makeup artists and soundtracks. In this case the researcher wants to conduct research on the characteristics of the film and the factors that make a film to be included in the IMDb Top 250. The data used in this study uses scraped data from the website. The method used is a non-hierarchical clustering method, namely kmeans and Dbscan. Where the Dbscan algorithm is used to determine the optimum number of clusters then proceed by grouping data based on centroids with k-means algorithm. From the analysis it was found that the factors that could influence a film included in the IMDB Top 250 were duration, number of votes, and films directed by Rajkumar Hirani and the optimal number of clusters using Dbscan algorithm obtained six clusters. With the improved k-means algorithm, the accuracy value for the cluster results is 87.2%.